Self-attention network for weak-supervised learning multi-material decomposition in dual energy CT

Abstract

Inspired by the deep learning techniques, data-driven methods have been developed to promote image quality and material decomposition accuracy in dual energy computed tomography (DECT) imaging. Most of these data-driven DECT imaging methods exploit the image priors within large amount of training data to learn the mapping function from the noisy DECT images to the desired high-quality material images in a supervised manner. Meanwhile, these supervised DECT imaging methods only estimate the multiple material images directly from the network but the material decomposition mechanism is not included in the network, and they fail to consider the unlabeled noisy DECT images to further improve the performance. In this work, to address these issues, we propose a novel Weak-supervised learning Multi-material Decomposition Network with self-attention mechanism (WMD-Net) to estimate multiple material images from DECT images with the combination of labeled and unlabeled DECT images accurately and effectively. Specifically, in the proposed WMD-Net, the labeled DECT images are used to estimate the three material images in a supervised sub-network, and the unlabeled DECT images are used to construct the unsupervised sub-network with the benefit of material decomposition mechanism. Finally, the two sub-networks are introduced into the proposed WMD-Net method. The proposed WMD-Net method is validated and evaluated through the synthesized clinical data, and the experimental results demonstrate that the proposed WMD-Net method can estimate more accurate material images than the other competing methods in terms of noise-induced artifacts reduction and structure details preservation.